Machine Vision

The biggest challenges of machine vision

How do you get a robot to automatically empty a bin with unsorted products and to sort all parts correctly on different pallets? For a person, such a task is child's play. The reason that this makes it so easy for us is because of our stereoscopic vision and the perfect eye-hand coordination that we evolved over millions of years..

This same development has been taking place in robotics for several years. Picking up and sorting, also known as bin-picking , is a big problem for a robot. The development of a robot system that is able to perform this task does not seem problematic at first glance. But this is often a misconception. Today, robots still lack the intelligence to solve problems like humans or have the ability to adapt to changing conditions.

To achieve this, the core-technolgy named machine vision is in development. Robots are equipped with one or several cameras,
Which the robot uses to capture, process and analyse images of its surroundings.

A technology with a lot of potential

This technology has the potential to far exceed human performance. When machine vision and bin-picking are fully developed, robots can record and analyze the parts in milliseconds. A check of the dimensions and weight is also possible with a speed that is physically out of the reach of man.

Machine Vision and Bin-Picking can only work well when software and hardware are fully matched. Unless the programmer manages to eliminate all bugs and software errors, this inevitably leads to malfunctions that the robot itself can not correct.

4 challenges

How can a robot manage to see and take actions autonomously? to achieve this, four challenges have to be solved:

The foundation for a successful image analysis is a high-quality image. High-resolution digital cameras with autofocus can do this effortlessly. Low resolution leads to disturbing elements and that makes the following analysis unnecessarily difficult. High quality images are a must.

When there are light sources or windows in the area of the robot, their influence on the image quality must be carefully examined. Light reflections can seriously influence the software during the analysis.

2. Automatic analyzing of images to obtain information

The image taken must be examined by special software in order to obtain the desired information from the image. The program must be able to distinguish the product from the assembly line, to recognize the shape, size and texture and to determine the exact position. The machine vision system can be supported by maintaining sufficient contrast between the desired product and the other products. If this contrast is too low, the shape and size of the parts can no longer be recognized correctly.

Problems may also occur when products have scratches on them. If these happen to be too big, there might be a chance that the software interprets this as the edge of an object

Next, an automatic analysis of the obtained information is made. The robot compares the image with information from a database. The dimensional accuracy and shape can be controlled relatively easily, because dimensions and weights are mathematically measurable. The robot sorts all parts separately if they deviate from the specified tolerance values.

Things become more complex when the robot has to assess the nature of surfaces. How big are scratches or damages? Is the part only dirty or are they material defects? As a solution to this, it is possible to train the system with images so that it can make the right decisions.

4. Sending the required signals to the robot arm

The result of the analysis must be passed on to the robot. The software then checks in advance in which position the gripper arm is located and communicates the necessary control pulses with each motor in the robot arm to reach the desired part, grasp it and then place it at the desired destination. If the machine vision system has made a mistake in the three previous steps, the robot will misunderstand.

Our future perspective

With machine vision and bin-picking, we have made enormous progress in recent years. The systems of Teqram are very reliable and developed so that several customers have now opted for product automation by our machine vision system. In the coming years we will continue to improve our technology and provide extra functions.

Machine Vision for your company

Many companies contain processes that can be automated. Production tasks can be tedious and often have to be repeated for hours. For employees these are tasks that are monotonous or physically exhausting. For such tasks, Teqram's product automation offers the right solution!

Make an appointment with us to learn more about the possibilities of machine vision and bin-picking. We actively support you through the entire automation process and guide you from planning to successful integration.